Vector space explorations of literary language

Abstract

Literary novels are said to distinguish themselves from other novels through conventions associated with literariness. We investigate the task of predicting the literariness of novels as perceived by readers, based on a large reader survey of contemporary Dutch novels. Previous research showed that ratings of literariness are predictable from texts to a substantial extent using machine learning, suggesting that it may be possible to explain the consensus among readers on which novels are literary as a consensus on the kind of writing style that characterizes literature. Although we have not yet collected human judgments to establish the influence of writing style directly (we use a survey with judgments based on the titles of novels), we can try to analyze the behavior of machine learning models on particular text fragments as a proxy for human judgments. In order to explore aspects of the texts associated with literariness, we divide the texts of the novels in chunks of 2-3 pages and create vector space representations using topic models (Latent Dirichlet Allocation) and neural document embeddings (Distributed Bag-of-Words Paragraph Vectors). We analyze the semantic complexity of the novels using distance measures, supporting the notion that literariness can be partly explained as a deviation from the norm. Furthermore, we build predictive models and identify specific keywords and stylistic markers related to literariness. While genre plays a role, we find that the greater part of factors affecting judgments of literariness are

Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

“…Topic modeling is a method used to analyze the content of texts by revealing hidden topics of documents in a collection. It has been used in computational studies of literary texts before, though with different objectives and background assumptions (van Cranenburgh et al, 2019 ). We are interested in the changes of topic distributions along a text, as it can be expected to have an impact on how “a reader progresses through a text with a growing understanding for its content, topics and themes” (Wallot et al, 2014 , p. 1749).…”

“…van Cranenburgh and Bod ( 2017 ) used frequency distributions of lexical and syntactic features to model human ratings of texts as more or less “literary” (see also Ashok et al, 2013 ; van Cranenburgh and Koolen, 2015 for similar approaches). In van Cranenburgh et al ( 2019 ), summary statistics derived from topic modeling (Latent Dirichlet Allocation) and paragraph vectors are used to predict degrees of “literariness.” Maharjan et al ( 2017 ) explore a wide variety of features (including “readability”) that can be used to classify texts in terms of “likability.” Other standard methods of computational linguistics used in this context include sentiment and emotion analysis (Alm and Sproat, 2005 ; Francisco and Gervás, 2006 ; Kakkonen and Galić Kakkonen, 2011 ; Mohammad, 2011 ; Reagan et al, 2016 ; Maharjan et al, 2018 ). Global statistical properties such as complexity and entropy have been used to study the regularity (Mehri and Lashkari, 2016 ; Hernández-Gómez et al, 2017 ) and the quality of texts (Febres and Jaffe, 2017 ).…”

“…van Cranenburgh and Bod (2017) used frequency distributions of lexical and syntactic features to model human ratings of texts as more or less "literary" (see also Ashok et al, 2013;van Cranenburgh and Koolen, 2015 for similar approaches). In van Cranenburgh et al (2019), summary statistics derived from topic modeling (Latent Dirichlet Allocation) and paragraph vectors are used to predict degrees of "literariness." Maharjan et al (2017) explore a wide variety of features (including "readability") that can be used to classify texts in terms of "likability."…”

“…An exception is provided byMcNerney et al, 2011, who had participants read a 361 pages long novel. For longer texts, human ratings have also been used as behavioral correlates of text structure (e.g.,van Cranenburgh and Bod, 2017;van Cranenburgh et al, 2019). A methodological toolbox for measuring reading experience has been proposed byKnoop et al (2016) andThissen et al (2018).…”

Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts

“…In a study of the update of machine learning in industry, Chiticariu et al [6] noted a gap in the volume of academic research on machine learning, compared with lower levels of uptake within industry and found the causes of this pertained to training data, interpretability and incorporation of domain knowledge. Similarly, the relatively low uptake of machine learning methods in the digital humanities has been attributed to issues pertaining to interpretation and trust [34,13,15]. Imparting domain knowledge into the process of text analysis through interpretation and annotation is also central to humanities research [17,37].…”